Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	142,910
2	Rhode Island	141,812
3	South Dakota	139,713
4	Utah	125,309
5	Tennessee	123,249
6	Arizona	119,687
7	Iowa	116,898
8	Nebraska	114,891
9	Wisconsin	114,800
10	Oklahoma	113,899
11	South Carolina	113,806
12	New Jersey	113,656
13	Arkansas	112,079
14	Delaware	109,451
15	Indiana	109,328
16	Alabama	108,683
17	Illinois	107,769
18	Kansas	107,583
19	New York	106,816
20	Florida	106,277
21	Idaho	106,216
22	Mississippi	105,676
23	Minnesota	104,889
24	Nevada	103,939
25	Montana	103,418
26	Georgia	102,553
27	Wyoming	101,935
28	Kentucky	101,625
29	Massachusetts	101,546
30	Texas	100,881
31	Louisiana	99,899
32	Missouri	99,037
33	Michigan	97,312
34	Connecticut	96,657
35	New Mexico	95,588
36	California	95,288
37	North Carolina	94,892
38	Alaska	94,027
39	Ohio	93,107
40	Pennsylvania	92,666
41	Colorado	92,280
42	West Virginia	87,953
43	Virginia	78,346
44	Maryland	75,249
45	New Hampshire	71,562
46	District of Columbia	68,585
47	Washington	55,323
48	Puerto Rico	53,068
49	Maine	48,387
50	Oregon	45,932
51	Vermont	38,019
52	Hawaii	23,896

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	259
2	Colorado	244
3	Maine	207
4	West Virginia	187
5	Pennsylvania	173
6	Florida	163
7	Washington	157
8	North Dakota	153
9	Montana	149
10	Oregon	149
11	Nevada	148
12	Minnesota	147
13	Illinois	141
14	Wyoming	133
15	Kentucky	130
16	Nebraska	128
17	North Carolina	124
18	New Hampshire	123
19	Alabama	122
20	Indiana	118
21	Utah	115
22	Idaho	114
23	Ohio	114
24	Delaware	112
25	Louisiana	110
26	Tennessee	110
27	South Dakota	108
28	Wisconsin	103
29	Alaska	102
30	Connecticut	99
31	District of Columbia	98
32	Massachusetts	98
33	New York	97
34	Vermont	97
35	Iowa	96
36	Mississippi	93
37	Georgia	92
38	Rhode Island	89
39	Texas	88
40	New Mexico	84
41	Arizona	77
42	Maryland	74
43	Arkansas	71
44	Missouri	71
45	Virginia	67
46	South Carolina	66
47	New Jersey	55
48	Kansas	50
49	California	41
50	Hawaii	40
51	Puerto Rico	34
52	Oklahoma	32

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,916
2	New York	2,690
3	Massachusetts	2,571
4	Rhode Island	2,546
5	Mississippi	2,435
6	Arizona	2,395
7	Connecticut	2,290
8	Louisiana	2,251
9	South Dakota	2,248
10	Alabama	2,243
11	Pennsylvania	2,089
12	North Dakota	2,011
13	Indiana	1,997
14	Michigan	1,965
15	New Mexico	1,962
16	Illinois	1,946
17	Arkansas	1,916
18	Iowa	1,901
19	South Carolina	1,868
20	Georgia	1,866
21	Nevada	1,793
22	Tennessee	1,787
23	Texas	1,758
24	Kansas	1,740
25	Oklahoma	1,738
26	Delaware	1,692
27	Florida	1,672
28	Ohio	1,663
29	California	1,583
30	District of Columbia	1,579
31	West Virginia	1,535
32	Missouri	1,527
33	Kentucky	1,507
34	Montana	1,494
35	Maryland	1,471
36	Wisconsin	1,323
37	Minnesota	1,306
38	Virginia	1,284
39	Nebraska	1,233
40	Wyoming	1,230
41	North Carolina	1,227
42	Idaho	1,156
43	Colorado	1,138
44	New Hampshire	975
45	Puerto Rico	755
46	Washington	744
47	Utah	703
48	Oregon	611
49	Maine	593
50	Alaska	459
51	Vermont	403
52	Hawaii	343

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	7
2	Pennsylvania	4
3	West Virginia	4
4	Colorado	3
5	New Jersey	3
6	Oklahoma	3
7	Puerto Rico	3
8	South Dakota	3
9	Utah	3
10	Wisconsin	3
11	Arkansas	2
12	California	2
13	Delaware	2
14	District of Columbia	2
15	Florida	2
16	Georgia	2
17	Illinois	2
18	Kentucky	2
19	Louisiana	2
20	Minnesota	2
21	Missouri	2
22	Nebraska	2
23	New Hampshire	2
24	New York	2
25	Oregon	2
26	Rhode Island	2
27	Texas	2
28	Virginia	2
29	Alabama	1
30	Alaska	1
31	Arizona	1
32	Connecticut	1
33	Indiana	1
34	Iowa	1
35	Kansas	1
36	Maryland	1
37	Massachusetts	1
38	Mississippi	1
39	Montana	1
40	Nevada	1
41	North Carolina	1
42	North Dakota	1
43	South Carolina	1
44	Tennessee	1
45	Vermont	1
46	Washington	1
47	Wyoming	1
48	Hawaii	0
49	Idaho	0
50	Maine	0
51	New Mexico	0
52	Ohio	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	370,954	1	99
Crowley	Colorado	362,151	2	99
Bent	Colorado	274,879	3	99
Dewey	South Dakota	250,170	4	99
Lincoln	Arkansas	246,007	5	99
Davidson	Tennessee	142,236	187	94
Richland	South Carolina	112,154	990	68
York	South Carolina	111,208	1028	67
Orange	California	85,330	2222	29
Pierce	Washington	57,534	2854	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,193	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,583	1814	42
York	South Carolina	1,363	2078	33
Davidson	Tennessee	1,356	2091	33
Richland	South Carolina	1,347	2101	33
Pierce	Washington	739	2736	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.